skneuromsi.core.ndresult.stats_acc module

Stats helper for the Result object.

class skneuromsi.core.ndresult.stats_acc.ResultStatsAccessor(result)[source]

Bases: AccessorABC

Calculate basic statistics of the result.

Kind of statistic to produce: - describe - count - mean - std - min - max - dimmin - dimmax - quantile

Parameters:

result (NDResult) – The NDResult object for which to calculate statistics.

count(*, modes=None, times=None, positions=None, coordinates=None)[source]

Count the number of elements in the NDResult.

Parameters:

  • modes (array-like or None, optional) – The modes to include in the count.

  • times (array-like or None, optional) – The times to include in the count.

  • positions (array-like or None, optional) – The positions to include in the count.

  • coordinates (array-like or None, optional) – The coordinates to include in the count.

Returns:

The number of elements in the filtered NDResult.

Return type:

int

mean(*, modes=None, times=None, positions=None, coordinates=None)[source]

Calculate the mean value of the NDResult.

Parameters:

  • modes (array-like or None, optional) – The modes to include in the mean calculation.

  • times (array-like or None, optional) – The times to include in the mean calculation.

  • positions (array-like or None, optional) – The positions to include in the mean calculation.

  • coordinates (array-like or None, optional) – The coordinates to include in the mean calculation.

Returns:

The mean value of the filtered NDResult.

Return type:

float

std(*, modes=None, times=None, positions=None, coordinates=None)[source]

Calculate the standard deviation of the NDResult.

Parameters:

  • modes (array-like or None, optional) – The modes to include in the standard deviation calculation.

  • times (array-like or None, optional) – The times to include in the standard deviation calculation.

  • positions (array-like or None, optional) – The positions to include in the standard deviation calculation.

  • coordinates (array-like or None, optional) – The coordinates to include in the standard deviation calculation.

Returns:

The standard deviation of the filtered NDResult.

Return type:

float

min(*, modes=None, times=None, positions=None, coordinates=None)[source]

Calculate the minimum value of the NDResult.

Parameters:

  • modes (array-like or None, optional) – The modes to include in the minimum value calculation.

  • times (array-like or None, optional) – The times to include in the minimum value calculation.

  • positions (array-like or None, optional) – The positions to include in the minimum value calculation.

  • coordinates (array-like or None, optional) – The coordinates to include in the minimum value calculation.

Returns:

The minimum value of the filtered NDResult.

Return type:

float

dimmin(*, modes=None, times=None, positions=None, coordinates=None)[source]

Calculate the minimum value and corresponding dimensions of the NDResult.

Parameters:

  • modes (array-like or None, optional) – The modes to include in the minimum value calculation.

  • times (array-like or None, optional) – The times to include in the minimum value calculation.

  • positions (array-like or None, optional) – The positions to include in the minimum value calculation.

  • coordinates (array-like or None, optional) – The coordinates to include in the minimum value calculation.

Returns:

A series containing the minimum value and corresponding dimensions of the filtered NDResult.

Return type:

pandas.Series

max(*, modes=None, times=None, positions=None, coordinates=None)[source]

Calculate the maximum value of the NDResult.

Parameters:

  • modes (array-like or None, optional) – The modes to include in the maximum value calculation.

  • times (array-like or None, optional) – The times to include in the maximum value calculation.

  • positions (array-like or None, optional) – The positions to include in the maximum value calculation.

  • coordinates (array-like or None, optional) – The coordinates to include in the maximum value calculation.

Returns:

The maximum value of the filtered NDResult.

Return type:

float

dimmax(*, modes=None, times=None, positions=None, coordinates=None)[source]

Calculate the maximum value and corresponding dimensions of the NDResult.

Parameters:

  • modes (array-like or None, optional) – The modes to include in the maximum value calculation.

  • times (array-like or None, optional) – The times to include in the maximum value calculation.

  • positions (array-like or None, optional) – The positions to include in the maximum value calculation.

  • coordinates (array-like or None, optional) – The coordinates to include in the maximum value calculation.

Returns:

A series containing the maximum value and corresponding dimensions of the filtered NDResult.

Return type:

pandas.Series

quantile(q=0.25, *, modes=None, times=None, positions=None, coordinates=None, **kwargs)[source]

Calculate the quantile value(s) of the NDResult.

Parameters:

  • q (float or array-like, optional) – The quantile(s) to calculate. Default is 0.25.

  • modes (array-like or None, optional) – The modes to include in the quantile calculation.

  • times (array-like or None, optional) – The times to include in the quantile calculation.

  • positions (array-like or None, optional) – The positions to include in the quantile calculation.

  • coordinates (array-like or None, optional) – The coordinates to include in the quantile calculation.

  • **kwargs – Additional keyword arguments to pass to xarray.DataArray.quantile().

Returns:

The calculated quantile value(s) of the filtered NDResult.

Return type:

numpy.ndarray

describe(percentiles=None)[source]

Generate descriptive statistics of the NDResult.

Parameters:

percentiles (array-like or None, optional) – The percentiles to include in the descriptive statistics. Default is [0.25, 0.5, 0.75].

Returns:

A DataFrame containing descriptive statistics of the NDResult.

Return type:

pandas.DataFrame